{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T00:15:08Z","timestamp":1784938508184,"version":"3.55.0"},"reference-count":93,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Power supply from renewable energy is an important part of modern power grids. Robust methods for predicting production are required to balance production and demand to avoid losses. This study proposed an approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used for wind power generation forecasting. LSTM networks perform notably well when addressing time-series prediction, and further hyperparameter tuning by a modified version of the reptile search algorithm (RSA) can help improve performance. The modified RSA was first evaluated against standard CEC2019 benchmark instances before being applied to the practical challenge. The proposed tuned LSTM model has been tested against two wind production datasets with hourly resolutions. The predictions were executed without and with decomposition for one, two, and three steps ahead. Simulation outcomes have been compared to LSTM networks tuned by other cutting-edge metaheuristics. It was observed that the introduced methodology notably exceed other contenders, as was later confirmed by the statistical analysis. Finally, this study also provides interpretations of the best-performing models on both observed datasets, accompanied by the analysis of the importance and impact each feature has on the predictions.<\/jats:p>","DOI":"10.1007\/s10462-023-10678-y","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T05:02:36Z","timestamp":1707714156000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting"],"prefix":"10.1007","volume":"57","author":[{"given":"Marijana","family":"Pavlov-Kagadejev","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luka","family":"Jovanovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nebojsa","family":"Bacanin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammet","family":"Deveci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miodrag","family":"Zivkovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milan","family":"Tuba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivana","family":"Strumberger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Witold","family":"Pedrycz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"10678_CR1","doi-asserted-by":"crossref","unstructured":"Abraham A, Guo H, Liu H (2006) Swarm intelligence: foundations, perspectives and applications. In: Swarm intelligent systems. Springer, pp 3\u201325","DOI":"10.1007\/978-3-540-33869-7_1"},{"key":"10678_CR2","doi-asserted-by":"crossref","first-page":"113609","DOI":"10.1016\/j.cma.2020.113609","volume":"376","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609","journal-title":"Comput Methods Appl Mech Eng"},{"key":"10678_CR3","doi-asserted-by":"publisher","first-page":"116158","DOI":"10.1016\/j.eswa.2021.116158","volume":"191","author":"L Abualigah","year":"2022","unstructured":"Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158. https:\/\/doi.org\/10.1016\/j.eswa.2021.116158","journal-title":"Expert Syst Appl"},{"issue":"2","key":"10678_CR4","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.renene.2008.05.002","volume":"34","author":"A Akella","year":"2009","unstructured":"Akella A, Saini R, Sharma MP (2009) Social, economical and environmental impacts of renewable energy systems. Renew Energy 34(2):390\u2013396","journal-title":"Renew Energy"},{"key":"10678_CR5","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1016\/j.egyr.2022.07.139","volume":"8","author":"I Amalou","year":"2022","unstructured":"Amalou I, Mouhni N, Abdali A (2022) Multivariate time series prediction by RNN architectures for energy consumption forecasting. Energy Rep 8:1084\u20131091. https:\/\/doi.org\/10.1016\/j.egyr.2022.07.139","journal-title":"Energy Rep"},{"key":"10678_CR6","unstructured":"Awerbuch S, Berger M (2003) Applying portfolio theory to EU electricity planning and policy-making, Sweden. https:\/\/www.osti.gov\/etdeweb\/biblio\/20354690"},{"key":"10678_CR7","doi-asserted-by":"crossref","unstructured":"Bacanin N, Bezdan T, Tuba E, Strumberger I, Tuba M, Zivkovic M (2019) Task scheduling in cloud computing environment by grey wolf optimizer. In: 2019 27th telecommunications forum (TELFOR). IEEE, pp 1\u20134","DOI":"10.1109\/TELFOR48224.2019.8971223"},{"key":"10678_CR8","first-page":"100711","volume":"35","author":"N Bacanin","year":"2022","unstructured":"Bacanin N, Sarac M, Budimirovic N, Zivkovic M, AlZubi AA, Bashir AK (2022a) Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization. Sustain Comput Inform Syst 35:100711","journal-title":"Sustain Comput Inform Syst"},{"issue":"22","key":"10678_CR9","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.3390\/math10224173","volume":"10","author":"N Bacanin","year":"2022","unstructured":"Bacanin N, Zivkovic M, Stoean C, Antonijevic M, Janicijevic S, Sarac M, Strumberger I (2022b) Application of natural language processing and machine learning boosted with swarm intelligence for spam email filtering. Mathematics 10(22):4173","journal-title":"Mathematics"},{"issue":"1","key":"10678_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-022-09744-2","volume":"12","author":"N Bacanin","year":"2022","unstructured":"Bacanin N, Zivkovic M, Al-Turjman F, Venkatachalam K, Trojovsk\u1ef3 P, Strumberger I, Bezdan T (2022c) Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application. Sci Rep 12(1):1\u201320","journal-title":"Sci Rep"},{"issue":"11","key":"10678_CR11","doi-asserted-by":"crossref","first-page":"4204","DOI":"10.3390\/s22114204","volume":"22","author":"N Bacanin","year":"2022","unstructured":"Bacanin N, Stoean C, Zivkovic M, Jovanovic D, Antonijevic M, Mladenovic D (2022d) Multi-swarm algorithm for extreme learning machine optimization. Sensors 22(11):4204","journal-title":"Sensors"},{"key":"10678_CR12","doi-asserted-by":"crossref","first-page":"104778","DOI":"10.1016\/j.micpro.2023.104778","volume":"98","author":"N Bacanin","year":"2023","unstructured":"Bacanin N, Venkatachalam K, Bezdan T, Zivkovic M, Abouhawwash M (2023a) A novel firefly algorithm approach for efficient feature selection with covid-19 dataset. Microprocess Microsyst 98:104778","journal-title":"Microprocess Microsyst"},{"issue":"3","key":"10678_CR13","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.3390\/en16031434","volume":"16","author":"N Bacanin","year":"2023","unstructured":"Bacanin N, Stoean C, Zivkovic M, Rakic M, Strulak-W\u00f3jcikiewicz R, Stoean R (2023b) On the benefits of using metaheuristics in the hyperparameter tuning of deep learning models for energy load forecasting. Energies 16(3):1434","journal-title":"Energies"},{"issue":"18","key":"10678_CR14","doi-asserted-by":"crossref","first-page":"4769","DOI":"10.3390\/en13184769","volume":"13","author":"J Belotti","year":"2020","unstructured":"Belotti J, Siqueira H, Araujo L, Stevan SL Jr, Mattos Neto PS, Marinho MH, Oliveira JFL, Usberti F, Leone Filho MdA, Converti A et al (2020) Neural-based ensembles and unorganized machines to predict streamflow series from hydroelectric plants. Energies 13(18):4769","journal-title":"Energies"},{"key":"10678_CR15","doi-asserted-by":"crossref","unstructured":"Beni G (2020) Swarm intelligence. In: Complex social and behavioral systems: game theory and agent-based models. Springer, pp 791\u2013818","DOI":"10.1007\/978-1-0716-0368-0_530"},{"key":"10678_CR16","doi-asserted-by":"crossref","unstructured":"Bezdan T, Zivkovic M, Tuba E, Strumberger I, Bacanin N, Tuba M (2020a) Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. In: International conference on intelligent and fuzzy systems. Springer, pp 718\u2013725","DOI":"10.1007\/978-3-030-51156-2_83"},{"key":"10678_CR17","doi-asserted-by":"crossref","unstructured":"Bezdan T, Zivkovic M, Antonijevic M, Zivkovic T, Bacanin N (2020b) Enhanced flower pollination algorithm for task scheduling in cloud computing environment. In: Machine learning for predictive analysis. Springer, pp 163\u2013171","DOI":"10.1007\/978-981-15-7106-0_16"},{"key":"10678_CR18","doi-asserted-by":"crossref","unstructured":"Bezdan T, Zivkovic M, Tuba E, Strumberger I, Bacanin N, Tuba M (2020c) Glioma brain tumor grade classification from MRI using convolutional neural networks designed by modified FA. In: International conference on intelligent and fuzzy systems. Springer, pp 955\u2013963","DOI":"10.1007\/978-3-030-51156-2_111"},{"key":"10678_CR19","doi-asserted-by":"crossref","unstructured":"Bezdan T, Cvetnic D, Gajic L, Zivkovic M, Strumberger I, Bacanin N (2021) Feature selection by firefly algorithm with improved initialization strategy. In: 7th conference on the engineering of computer based systems. pp 1\u20138","DOI":"10.1145\/3459960.3459974"},{"issue":"6","key":"10678_CR20","doi-asserted-by":"crossref","first-page":"061815","DOI":"10.1117\/1.JEI.31.6.061815","volume":"31","author":"M Bukumira","year":"2022","unstructured":"Bukumira M, Antonijevic M, Jovanovic D, Zivkovic M, Mladenovic D, Kunjadic G (2022) Carrot grading system using computer vision feature parameters and a cascaded graph convolutional neural network. J Electron Imaging 31(6):061815","journal-title":"J Electron Imaging"},{"key":"10678_CR21","doi-asserted-by":"crossref","unstructured":"Cheng S, Shi Y (2011) Diversity control in particle swarm optimization. In: 2011 IEEE symposium on swarm intelligence. IEEE, pp 1\u20139","DOI":"10.1109\/SIS.2011.5952581"},{"issue":"1","key":"10678_CR22","doi-asserted-by":"crossref","first-page":"9138","DOI":"10.1038\/s41598-023-36379-8","volume":"13","author":"D Coppitters","year":"2023","unstructured":"Coppitters D, Contino F (2023) Optimizing upside variability and antifragility in renewable energy system design. Sci Rep 13(1):9138","journal-title":"Sci Rep"},{"issue":"1","key":"10678_CR23","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","volume":"1","author":"J Derrac","year":"2011","unstructured":"Derrac J, Garc\u00eda S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3\u201318","journal-title":"Swarm Evol Comput"},{"issue":"16","key":"10678_CR24","doi-asserted-by":"publisher","first-page":"12391","DOI":"10.1007\/s00500-020-04680-7","volume":"24","author":"AS Devi","year":"2020","unstructured":"Devi AS, Maragatham G, Boopathi K, Rangaraj A (2020) Hourly day-ahead wind power forecasting with the EEMD-CSO-LSTM-EFG deep learning technique. Soft Comput 24(16):12391\u201312411. https:\/\/doi.org\/10.1007\/s00500-020-04680-7","journal-title":"Soft Comput"},{"key":"10678_CR95","doi-asserted-by":"publisher","unstructured":"Din\u00e7er H, Y\u00fcksel S, Eti S (2023) Identifying the right policies for increasing the efficiency of the renewable energy transition with a novel fuzzy decision-making model. J Soft Comput Decis Anal 1(1):50\u201362. https:\/\/doi.org\/10.31181\/jscda1120234","DOI":"10.31181\/jscda1120234"},{"issue":"4","key":"10678_CR25","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28\u201339","journal-title":"IEEE Comput Intell Mag"},{"issue":"3","key":"10678_CR26","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2013","unstructured":"Dragomiretskiy K, Zosso D (2013) Variational mode decomposition. IEEE Trans Signal Process 62(3):531\u2013544","journal-title":"IEEE Trans Signal Process"},{"issue":"9","key":"10678_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3561048","volume":"55","author":"R Dwivedi","year":"2023","unstructured":"Dwivedi R, Dave D, Naik H, Singhal S, Omer R, Patel P, Qian B, Wen Z, Shah T, Morgan G et al (2023) Explainable AI (XAI): core ideas, techniques, and solutions. ACM Comput Surv 55(9):1\u201333","journal-title":"ACM Comput Surv"},{"key":"10678_CR28","unstructured":"Eftimov T, Koro\u0161ec P, Seljak BK (2016) Disadvantages of statistical comparison of stochastic optimization algorithms. In: Proceedings of the bioinspired optimizaiton methods and their applications, BIOMA. pp 105\u2013118"},{"key":"10678_CR29","doi-asserted-by":"crossref","unstructured":"Emmerich M, Shir OM, Wang H (2018) Evolution strategies. In: Handbook of heuristics. Springer, pp 89\u2013119","DOI":"10.1007\/978-3-319-07124-4_13"},{"issue":"1","key":"10678_CR30","doi-asserted-by":"publisher","first-page":"175","DOI":"10.3390\/w12010175","volume":"12","author":"H Fan","year":"2020","unstructured":"Fan H, Jiang M, Xu L, Zhu H, Cheng J, Jiang J (2020) Comparison of long short term memory networks and the hydrological model in runoff simulation. Water 12(1):175. https:\/\/doi.org\/10.3390\/w12010175","journal-title":"Water"},{"issue":"1","key":"10678_CR31","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s10462-018-09676-2","volume":"53","author":"F Fausto","year":"2020","unstructured":"Fausto F, Reyna-Orta A, Cuevas E, Andrade \u00c1G, Perez-Cisneros M (2020) From ants to whales: metaheuristics for all tastes. Artif Intell Rev 53(1):753\u2013810","journal-title":"Artif Intell Rev"},{"issue":"200","key":"10678_CR32","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675\u2013701","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"10678_CR33","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86\u201392","journal-title":"Ann Math Stat"},{"key":"10678_CR34","doi-asserted-by":"publisher","first-page":"57078","DOI":"10.1109\/ACCESS.2019.2912621","volume":"7","author":"J Fu","year":"2019","unstructured":"Fu J, Chu J, Guo P, Chen Z (2019) Condition monitoring of wind turbine gearbox bearing based on deep learning model. IEEE Access 7:57078\u201357087. https:\/\/doi.org\/10.1109\/ACCESS.2019.2912621","journal-title":"IEEE Access"},{"key":"10678_CR35","doi-asserted-by":"crossref","unstructured":"Gajic L, Cvetnic D, Zivkovic M, Bezdan T, Bacanin N, Milosevic S (2021) Multi-layer perceptron training using hybridized bat algorithm. In: Computational vision and bio-inspired computing. Springer, pp 689\u2013705","DOI":"10.1007\/978-981-33-6862-0_54"},{"key":"10678_CR36","doi-asserted-by":"crossref","first-page":"101039","DOI":"10.1016\/j.ecoinf.2019.101039","volume":"56","author":"MV Garc\u00eda","year":"2020","unstructured":"Garc\u00eda MV, Aznarte JL (2020) Shapley additive explanations for NO2 forecasting. Eco Inform 56:101039","journal-title":"Eco Inform"},{"key":"10678_CR37","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849\u2013872","journal-title":"Future Gener Comput Syst"},{"issue":"8","key":"10678_CR38","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"issue":"2","key":"10678_CR39","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.ijforecast.2013.07.001","volume":"30","author":"T Hong","year":"2014","unstructured":"Hong T, Pinson P, Fan S (2014) Global energy forecasting competition 2012. Int J Forecast 30(2):357\u2013363. https:\/\/doi.org\/10.1016\/j.ijforecast.2013.07.001","journal-title":"Int J Forecast"},{"issue":"1971","key":"10678_CR40","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1998","unstructured":"Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A Math Phys Eng Sci 454(1971):903\u2013995","journal-title":"Proc R Soc Lond Ser A Math Phys Eng Sci"},{"issue":"6","key":"10678_CR41","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1080\/03610928008827904","volume":"9","author":"RL Iman","year":"1980","unstructured":"Iman RL, Davenport JM (1980) Approximations of the critical region of the fbietkan statistic. Commun Stat Theory Methods 9(6):571\u2013595","journal-title":"Commun Stat Theory Methods"},{"issue":"21","key":"10678_CR42","doi-asserted-by":"crossref","first-page":"14616","DOI":"10.3390\/su142114616","volume":"14","author":"L Jovanovic","year":"2022","unstructured":"Jovanovic L, Jovanovic D, Bacanin N, Jovancai Stakic A, Antonijevic M, Magd H, Thirumalaisamy R, Zivkovic M (2022a) Multi-step crude oil price prediction based on LSTM approach tuned by salp swarm algorithm with disputation operator. Sustainability 14(21):14616","journal-title":"Sustainability"},{"issue":"13","key":"10678_CR43","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.3390\/math10132272","volume":"10","author":"D Jovanovic","year":"2022","unstructured":"Jovanovic D, Antonijevic M, Stankovic M, Zivkovic M, Tanaskovic M, Bacanin N (2022b) Tuning machine learning models using a group search firefly algorithm for credit card fraud detection. Mathematics 10(13):2272","journal-title":"Mathematics"},{"issue":"1","key":"10678_CR44","doi-asserted-by":"crossref","first-page":"109","DOI":"10.3390\/atmos14010109","volume":"14","author":"L Jovanovic","year":"2023","unstructured":"Jovanovic L, Jovanovic G, Perisic M, Alimpic F, Stanisic S, Bacanin N, Zivkovic M, Stojic A (2023a) The explainable potential of coupling metaheuristics-optimized-XGBoost and SHAP in revealing VOCS\u2019 environmental fate. Atmosphere 14(1):109","journal-title":"Atmosphere"},{"key":"10678_CR45","doi-asserted-by":"crossref","unstructured":"Jovanovic L, Djuric M, Zivkovic M, Jovanovic D, Strumberger I, Antonijevic M, Budimirovic N, Bacanin N (2023b) Tuning XGBoost by planet optimization algorithm: an application for diabetes classification. In: Proceedings of fourth international conference on communication, computing and electronics systems: ICCCES 2022. Springer, pp 787\u2013803","DOI":"10.1007\/978-981-19-7753-4_60"},{"issue":"1","key":"10678_CR46","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.asoc.2007.05.007","volume":"8","author":"D Karaboga","year":"2008","unstructured":"Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687\u2013697","journal-title":"Appl Soft Comput"},{"key":"10678_CR47","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995-international conference on neural networks, vol 4. IEEE, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"10678_CR48","doi-asserted-by":"crossref","first-page":"113338","DOI":"10.1016\/j.eswa.2020.113338","volume":"149","author":"M Khishe","year":"2020","unstructured":"Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338","journal-title":"Expert Syst Appl"},{"key":"10678_CR49","doi-asserted-by":"crossref","first-page":"100973","DOI":"10.1016\/j.swevo.2021.100973","volume":"67","author":"A LaTorre","year":"2021","unstructured":"LaTorre A, Molina D, Osaba E, Poyatos J, Del Ser J, Herrera F (2021) A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm Evol Comput 67:100973","journal-title":"Swarm Evol Comput"},{"key":"10678_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2023.05.133","author":"M Li","year":"2023","unstructured":"Li M, Yao J, Shen Y, Yuan B, Simmonds I, Liu Y (2023) Impact of synoptic circulation patterns on renewable energy-related variables over China. Renew Energy. https:\/\/doi.org\/10.1016\/j.renene.2023.05.133","journal-title":"Renew Energy"},{"issue":"6","key":"10678_CR51","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.3390\/app9061108","volume":"9","author":"Y Liu","year":"2019","unstructured":"Liu Y, Guan L, Hou C, Han H, Liu Z, Sun Y, Zheng M (2019) Wind power short-term prediction based on LSTM and discrete wavelet transform. Appl Sci 9(6):1108","journal-title":"Appl Sci"},{"issue":"18","key":"10678_CR52","doi-asserted-by":"publisher","first-page":"4964","DOI":"10.3390\/en13184964","volume":"13","author":"B Liu","year":"2020","unstructured":"Liu B, Zhao S, Yu X, Zhang L, Wang Q (2020) A novel deep learning approach for wind power forecasting based on WD-LSTM model. Energies 13(18):4964. https:\/\/doi.org\/10.3390\/en13184964","journal-title":"Energies"},{"key":"10678_CR53","first-page":"4765","volume-title":"Advances in neural information processing systems","author":"SM Lundberg","year":"2017","unstructured":"Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol 30. Curran Associates Inc., New York, pp 4765\u20134774"},{"key":"10678_CR54","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.ins.2021.09.054","volume":"581","author":"PS Mattos Neto","year":"2021","unstructured":"Mattos Neto PS, Oliveira JF, de O. Santos J\u00fanior DS, Siqueira HV, Marinho MH, Madeiro F (2021) An adaptive hybrid system using deep learning for wind speed forecasting. Inf Sci 581:495\u2013514","journal-title":"Inf Sci"},{"key":"10678_CR55","doi-asserted-by":"crossref","unstructured":"Milosevic S, Bezdan T, Zivkovic M, Bacanin N, Strumberger I, Tuba M (2021) Feed-forward neural network training by hybrid bat algorithm. In: Modelling and development of intelligent systems: 7th international conference, MDIS 2020, Sibiu, Romania, October 22\u201324, 2020, revised selected papers 7. Springer International Publishing, pp 52\u201366","DOI":"10.1007\/978-3-030-68527-0_4"},{"key":"10678_CR56","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","volume":"96","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120\u2013133","journal-title":"Knowl Based Syst"},{"key":"10678_CR57","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"key":"10678_CR58","doi-asserted-by":"crossref","unstructured":"Petrovic A, Bacanin N, Zivkovic M, Marjanovic M, Antonijevic M, Strumberger I (2022) The AdaBoost approach tuned by firefly metaheuristics for fraud detection. In: 2022 IEEE world conference on applied intelligence and computing (AIC). IEEE, pp 834\u2013839","DOI":"10.1109\/AIC55036.2022.9848902"},{"key":"10678_CR59","doi-asserted-by":"publisher","unstructured":"Preuss M, Stoean C, Stoean R (2011) Niching foundations: basin identification on fixed-property generated landscapes. In: Proceedings of the 13th annual conference on genetic and evolutionary computation. GECCO \u201911. Association for Computing Machinery, New York, pp 837\u2013844. https:\/\/doi.org\/10.1145\/2001576.2001691","DOI":"10.1145\/2001576.2001691"},{"issue":"1","key":"10678_CR60","first-page":"1934","volume":"20","author":"P Probst","year":"2019","unstructured":"Probst P, Boulesteix A-L, Bischl B (2019) Tunability: importance of hyperparameters of machine learning algorithms. J Mach Learn Res 20(1):1934\u20131965","journal-title":"J Mach Learn Res"},{"key":"10678_CR61","doi-asserted-by":"publisher","unstructured":"Rahnamayan S, Tizhoosh HR, Salama MMA (2007) Quasi-oppositional differential evolution. In: 2007 IEEE congress on evolutionary computation. pp 2229\u20132236. https:\/\/doi.org\/10.1109\/CEC.2007.4424748","DOI":"10.1109\/CEC.2007.4424748"},{"key":"10678_CR62","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.renene.2020.09.042","volume":"164","author":"A Razmjoo","year":"2021","unstructured":"Razmjoo A, Kaigutha LG, Rad MV, Marzband M, Davarpanah A, Denai M (2021) A technical analysis investigating energy sustainability utilizing reliable renewable energy sources to reduce CO2 emissions in a high potential area. Renew Energy 164:46\u201357","journal-title":"Renew Energy"},{"issue":"23","key":"10678_CR63","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1109\/TSP.2019.2951223","volume":"67","author":"N Rehman","year":"2019","unstructured":"Rehman N, Aftab H (2019) Multivariate variational mode decomposition. IEEE Trans Signal Process 67(23):6039\u20136052","journal-title":"IEEE Trans Signal Process"},{"issue":"2117","key":"10678_CR64","first-page":"1291","volume":"466","author":"N Rehman","year":"2010","unstructured":"Rehman N, Mandic DP (2010) Multivariate empirical mode decomposition. Proc R Soc A Math Phys Eng Sci 466(2117):1291\u20131302","journal-title":"Proc R Soc A Math Phys Eng Sci"},{"key":"10678_CR65","doi-asserted-by":"crossref","unstructured":"Salb M, Jovanovic L, Zivkovic M, Tuba E, Elsadai A, Bacanin N (2022) Training logistic regression model by enhanced moth flame optimizer for spam email classification. In: Computer networks and inventive communication technologies: proceedings of fifth ICCNCT 2022. Springer, pp 753\u2013768","DOI":"10.1007\/978-981-19-3035-5_56"},{"key":"10678_CR66","doi-asserted-by":"crossref","first-page":"120069","DOI":"10.1016\/j.energy.2021.120069","volume":"223","author":"F Shahid","year":"2021","unstructured":"Shahid F, Zameer A, Muneeb M (2021) A novel genetic LSTM model for wind power forecast. Energy 223:120069","journal-title":"Energy"},{"key":"10678_CR67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/4874757","volume":"2021","author":"B Shao","year":"2021","unstructured":"Shao B, Song D, Bian G, Zhao Y (2021) Wind speed forecast based on the LSTM neural network optimized by the firework algorithm. Adv Mater Sci Eng 2021:1\u201313","journal-title":"Adv Mater Sci Eng"},{"issue":"337","key":"10678_CR68","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/01621459.1972.10481232","volume":"67","author":"SS Shapiro","year":"1972","unstructured":"Shapiro SS, Francia R (1972) An approximate analysis of variance test for normality. J Am Stat Assoc 67(337):215\u2013216","journal-title":"J Am Stat Assoc"},{"key":"10678_CR69","doi-asserted-by":"crossref","DOI":"10.1201\/9780429186196","volume-title":"Handbook of parametric and nonparametric statistical procedures","author":"DJ Sheskin","year":"2020","unstructured":"Sheskin DJ (2020) Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca Raton"},{"issue":"5","key":"10678_CR70","doi-asserted-by":"crossref","first-page":"3471","DOI":"10.1016\/j.rser.2012.02.044","volume":"16","author":"J Shi","year":"2012","unstructured":"Shi J, Guo J, Zheng S (2012) Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renew Sustain Energy Rev 16(5):3471\u20133480","journal-title":"Renew Sustain Energy Rev"},{"key":"10678_CR71","doi-asserted-by":"crossref","unstructured":"Stankovic M, Antonijevic M, Bacanin N, Zivkovic M, Tanaskovic M, Jovanovic D (2022) Feature selection by hybrid artificial bee colony algorithm for intrusion detection. In: 2022 international conference on edge computing and applications (ICECAA). IEEE, pp 500\u2013505","DOI":"10.1109\/ICECAA55415.2022.9936116"},{"key":"10678_CR72","doi-asserted-by":"publisher","DOI":"10.1007\/s11047-020-09824-0","author":"H Stegherr","year":"2020","unstructured":"Stegherr H, Heider M, H\u00e4hner J (2020) Classifying metaheuristics: towards a unified multi-level classification system. Nat Comput. https:\/\/doi.org\/10.1007\/s11047-020-09824-0","journal-title":"Nat Comput"},{"issue":"3","key":"10678_CR73","doi-asserted-by":"crossref","first-page":"266","DOI":"10.3390\/axioms12030266","volume":"12","author":"C Stoean","year":"2023","unstructured":"Stoean C, Zivkovic M, Bozovic A, Bacanin N, Strulak-W\u00f3jcikiewicz R, Antonijevic M, Stoean R (2023) Metaheuristic-based hyperparameter tuning for recurrent deep learning: application to the prediction of solar energy generation. Axioms 12(3):266","journal-title":"Axioms"},{"key":"10678_CR74","doi-asserted-by":"crossref","unstructured":"Strumberger I, Tuba E, Zivkovic M, Bacanin N, Beko M, Tuba M (2019) Dynamic search tree growth algorithm for global optimization. In: Doctoral conference on computing, electrical and industrial systems. Springer, pp 143\u2013153","DOI":"10.1007\/978-3-030-17771-3_12"},{"key":"10678_CR75","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-022-00764-5","author":"M Tayebi","year":"2022","unstructured":"Tayebi M, El Kafhali S (2022) Performance analysis of metaheuristics based hyperparameters optimization for fraud transactions detection. Evol Intell. https:\/\/doi.org\/10.1007\/s12065-022-00764-5","journal-title":"Evol Intell"},{"key":"10678_CR76","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2022.1076529","author":"N Wang","year":"2023","unstructured":"Wang N, Li Z (2023) Short term power load forecasting based on BES-VMD and CNN-Bi-LSTM method with error correction. Front Energy Res. https:\/\/doi.org\/10.3389\/fenrg.2022.1076529","journal-title":"Front Energy Res"},{"issue":"15","key":"10678_CR77","doi-asserted-by":"publisher","first-page":"5744","DOI":"10.3390\/s22155744","volume":"22","author":"L Wang","year":"2022","unstructured":"Wang L, Liu H, Pan Z, Fan D, Zhou C, Wang Z (2022a) Long short-term memory neural network with transfer learning and ensemble learning for remaining useful life prediction. Sensors 22(15):5744. https:\/\/doi.org\/10.3390\/s22155744","journal-title":"Sensors"},{"issue":"12","key":"10678_CR78","doi-asserted-by":"publisher","first-page":"7307","DOI":"10.3390\/su14127307","volume":"14","author":"D Wang","year":"2022","unstructured":"Wang D, Cui X, Niu D (2022b) Wind power forecasting based on LSTM improved by EMD-PCA-RF. Sustainability 14(12):7307. https:\/\/doi.org\/10.3390\/su14127307","journal-title":"Sustainability"},{"issue":"1","key":"10678_CR79","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"key":"10678_CR80","doi-asserted-by":"crossref","unstructured":"Yang X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, pp 169\u2013178","DOI":"10.1007\/978-3-642-04944-6_14"},{"key":"10678_CR81","doi-asserted-by":"crossref","unstructured":"Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65\u201374","DOI":"10.1007\/978-3-642-12538-6_6"},{"issue":"5","key":"10678_CR82","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1108\/02644401211235834","volume":"29","author":"X-S Yang","year":"2012","unstructured":"Yang X-S, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464\u201383","journal-title":"Eng Comput"},{"key":"10678_CR96","doi-asserted-by":"publisher","unstructured":"Y\u00fcksel S, Eti S, Din\u00e7er H, G\u00f6kalp Y (2024) Comprehensive risk analysis and decision-making model for hydroelectricity energy investments. J Soft Comput Decis Anal 2(1):28\u201338. https:\/\/doi.org\/10.31181\/jscda2120242","DOI":"10.31181\/jscda2120242"},{"key":"10678_CR83","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.enconman.2016.01.023","volume":"112","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Liu K, Qin L, An X (2016) Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods. Energy Convers Manag 112:208\u2013219","journal-title":"Energy Convers Manag"},{"key":"10678_CR84","doi-asserted-by":"publisher","first-page":"120797","DOI":"10.1016\/j.energy.2021.120797","volume":"229","author":"T Zhang","year":"2021","unstructured":"Zhang T, Tang Z, Wu J, Du X, Chen K (2021) Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm. Energy 229:120797. https:\/\/doi.org\/10.1016\/j.energy.2021.120797","journal-title":"Energy"},{"key":"10678_CR85","doi-asserted-by":"crossref","unstructured":"Zivkovic M, Bacanin N, Tuba E, Strumberger I, Bezdan T, Tuba M (2020) Wireless sensor networks life time optimization based on the improved firefly algorithm. In: 2020 international wireless communications and mobile computing (IWCMC). IEEE, pp 1176\u20131181","DOI":"10.1109\/IWCMC48107.2020.9148087"},{"key":"10678_CR86","doi-asserted-by":"crossref","first-page":"102669","DOI":"10.1016\/j.scs.2020.102669","volume":"66","author":"M Zivkovic","year":"2021","unstructured":"Zivkovic M, Bacanin N, Venkatachalam K, Nayyar A, Djordjevic A, Strumberger I, Al-Turjman F (2021a) Covid-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain Cities Soc 66:102669","journal-title":"Sustain Cities Soc"},{"key":"10678_CR87","doi-asserted-by":"crossref","unstructured":"Zivkovic M, Venkatachalam K, Bacanin N, Djordjevic A, Antonijevic M, Strumberger I, Rashid TA (2021b) Hybrid genetic algorithm and machine learning method for covid-19 cases prediction. In: Proceedings of international conference on sustainable expert systems: ICSES 2020, vol 176. Springer Nature, p 169","DOI":"10.1007\/978-981-33-4355-9_14"},{"key":"10678_CR88","doi-asserted-by":"crossref","unstructured":"Zivkovic M, Bezdan T, Strumberger I, Bacanin N, Venkatachalam K (2021c) Improved Harris Hawks optimization algorithm for workflow scheduling challenge in cloud\u2013edge environment. In: Computer networks, Big Data and IoT. Springer, pp 87\u2013102","DOI":"10.1007\/978-981-16-0965-7_9"},{"key":"10678_CR89","doi-asserted-by":"crossref","unstructured":"Zivkovic M, Zivkovic T, Venkatachalam K, Bacanin N (2021d) Enhanced dragonfly algorithm adapted for wireless sensor network lifetime optimization. In: Data intelligence and cognitive informatics. Springer, pp 803\u2013817","DOI":"10.1007\/978-981-15-8530-2_63"},{"issue":"22","key":"10678_CR90","doi-asserted-by":"crossref","first-page":"3798","DOI":"10.3390\/electronics11223798","volume":"11","author":"M Zivkovic","year":"2022","unstructured":"Zivkovic M, Bacanin N, Antonijevic M, Nikolic B, Kvascev G, Marjanovic M, Savanovic N (2022) Hybrid CNN and XGBoost model tuned by modified arithmetic optimization algorithm for COVID-19 early diagnostics from X-ray images. Electronics 11(22):3798","journal-title":"Electronics"},{"key":"10678_CR91","doi-asserted-by":"crossref","first-page":"785908","DOI":"10.3389\/fenrg.2021.785908","volume":"9","author":"A Z\u00fcttel","year":"2022","unstructured":"Z\u00fcttel A, Gallandat N, Dyson PJ, Schlapbach L, Gilgen PW, Orimo S-I (2022) Future Swiss energy economy: the challenge of storing renewable energy. Front Energy Res 9:785908","journal-title":"Front Energy Res"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10678-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10678-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10678-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T21:31:48Z","timestamp":1709933508000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10678-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,12]]},"references-count":93,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["10678"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10678-y","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,12]]},"assertion":[{"value":"20 December 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"45"}}